Prediction of groundwater level fluctuations under climate change based on machine learning algorithms in the Mashhad aquifer, Iran

Abstract

Abstract The purpose of this study is the projection of climate change's impact on the Groundwater Level (GWL) fluctuations in the Mashhad aquifer during the future period (2022–2064). In the first step, the climatic variables using ACCESS-CM2 model under the Shared Socio-economic Pathways (SSPs) 5–8.5 scenario were extracted. In the second step, different machine learning algorithms, including Multilayer Perceptron Neural Network (MLP), Adaptive Neuro-fuzzy Inference System Neutral Network (ANFIS), Radial Basis Function Neural Network (RBF), and Support Vector Machine (SVM) were employed for the GWL fluctuations time series prediction under climate change in the future. Our results point out that temperatures and evaporation will increase in the autumn season, and precipitation will decrease by 26%. The amount of evaporation will increase in the winter due to an increase in temperature and a decrease in precipitation. The results showed that the RBFNN model had an excellent performance in predicting GWL compared to other models due to the highest value of R² (R² = 0.99) and the lowest value of RMSE, which were 0.05 and 0.06 meters in training and testing steps, respectively. Based on the result of the RBFNN model, the GWL will decrease by 6.60 meters under the SSP5-8.5 scenario.